The ‘rgr’ package for the R Open Source statistical computing and graphics environment - a tool to support geochemical data interpretation
Bibliographic record
Abstract
The development of interactive computer graphics to support applied geochemistry over the last 40 years at the Geological Survey of Canada (GSC) is briefly discussed. The loss of an interactive computing environment, IDEAS, in 1995 based on a DEC VAX computer largely negated nine years of work, though the experience gained was invaluable. The availability of the commercial S-PLUS package in a Windows PC environment led to the redevelopment of most of the functionality of IDEAS in the S language for statistical analysis and graphics. In 2006 a request from a sister federal government department for the S-PLUS software led to the decision to translate the S functions into R, an Open-Source implementation of the S language, and therefore free to the user. Since that time all development has been in R, resulting in the 2007 release to the public of a package of tools, ‘rgr’, to assist applied geochemists in interpreting their data. Subsequently, ‘rgr’ has been updated and extended. The move to Open Source R and the release of the ‘rgr’ package on the Comprehensive R Archival Network (CRAN) has made these tools, and their documentation, available for Windows, Unix and Mac computing environments. The paper outlines the features of ‘rgr’ and illustrates key graphic and tabular displays. Its functionality is reviewed in the context of earlier GSC interactive graphics packages. Supplementary Material: this is available at http://www.geolsoc.org.uk/SUP18713
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.090 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.155 | 0.107 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".